The present invention is related to autonomous robotic manipulation of objects. Currently, mobile manipulators are often constrained to narrowly defined missions and environments or require constant supervision from a human operator. Despite recent advances in building adaptive and autonomous robots, combining the two remains a difficult challenge. Although analytic solutions provide great precision and repeatability, they suffer from many problems: uncertainty in the sensory input, high sensitivity in sensory-motor calibration, and an inability to perceive and analytically model natural environments. On the other hand, learning systems, which are by design adaptable, suffer from the curse of dimensionality. Learning a task from scratch is impracticable in that the number of possible movement combinations is exponential in the number of degrees of freedom of the robot.
For example, a disadvantage of prior work using such state machines is that they are either composed of rigid actions (e.g., go to end position x, y, z) or involve exploratory planning for a movements between two locations (see, for example, the STRIPS, Stanford Research Institute Problem Solver, architecture) (see the List of Incorporated Cited Literature References, Literature Reference No. 4). Rigid actions do not adapt to obstacles or moving targets. On the other hand, traditional planning suffers from the combinatorial explosion of configurations, particularly for tasks requiring precise coordination between the arms. In most planning methods, like Rapidly-exploring Random Trees (see Literature Reference No. 2), the computation time increases exponentially with the number of degrees-of-freedom of the robot (see Literature Reference Nos. 3 and 5).
Each of the prior methods described above exhibit limitations that make them incomplete. Thus, a continuing need exists for a system using state machines that allows for complex and autonomous robotic operation without the need for computationally extensive planning.